In this report, we reproduce the analyses using data from behavioral study 2 reported in Supplementary Material.

prep data

First, we load the relevant packages, define functions and plotting aesthetics, and load and tidy the data.

load packages

if(!require('pacman')) {
    install.packages('pacman')
}

pacman::p_load(tidyverse, purrr, fs, knitr, lmerTest, ggeffects, kableExtra, boot, devtools, EMAtools, install = TRUE)
devtools::install_github("hadley/emo")

define functions

# MLM results table function
table_model = function(model_data, reversed = FALSE) {
  results = model_data %>%
  broom.mixed::tidy(conf.int = TRUE) %>%
  filter(effect == "fixed") %>%
  rename("SE" = std.error,
         "t" = statistic,
         "p" = p.value) %>%
  select(-group, -effect) %>%
  mutate_at(vars(-contains("term"), -contains("p")), round, 2) %>%
  mutate(term = gsub("cond", "", term),
         term = gsub("\\(Intercept\\)", "intercept", term),
         term = gsub("article_other", "other", term),
         term = gsub("article_self", "self", term),
         term = gsub("topichealth", "topic (health)", term),
         term = gsub("msg_rel_self_z", "self-relevance", term),
         term = gsub("msg_rel_social_z", "social relevance", term),
         term = gsub("sharing_type", "sharing type (broadcast)", term),
         term = gsub(":", " x ", term),
         p = ifelse(p < .001, "< .001",
             ifelse(p > .999, "1.000", gsub("0.(.*)", ".\\1", sprintf("%.3f", p)))),
         `b [95% CI]` = sprintf("%.2f [%0.2f, %.2f]", estimate, conf.low, conf.high)) %>%
  select(term, `b [95% CI]`, df, t, p)
  
  if (reversed == TRUE) {
    results = results %>%
      mutate(term = gsub("broadcast", "narrowcast", term))
  }
  
  results %>%
      select(term, `b [95% CI]`, df, t, p)
}

# simple effects function
simple_effects = function(model, sharing = FALSE) {
  if(sharing == FALSE) {
    results = emmeans::contrast(emmeans::emmeans(model, ~ article_cond | group),
                            "revpairwise", by = "group", adjust = "none") %>%
      data.frame() %>%
      filter(grepl("control", contrast)) %>%
      select(contrast, group, estimate, p.value)
  } else {
    results = emmeans::contrast(emmeans::emmeans(model, ~ article_cond | group + sharing_type),
                            "revpairwise", by = "group", adjust = "none") %>%
      data.frame() %>%
      filter(grepl("- control", contrast)) %>%
      filter(!grepl("^control", contrast)) %>%
      extract(contrast, c("exp_sharing", "control_sharing"), ".* (0|1) - control (0|1)", remove = FALSE) %>%
      filter(exp_sharing == control_sharing) %>%
      mutate(sharing_type = ifelse(exp_sharing == 0, "broadcast", "narrowcast"),
             contrast = gsub("0|1", "", contrast)) %>%
      select(contrast, sharing_type, group, estimate, p.value)
  }
  
  results %>%
    mutate(p.value = ifelse(p.value < .001, "< .001",
                      ifelse(p.value == 1, "1.000", gsub("0.(.*)", ".\\1", sprintf("%.3f", p.value))))) %>%
    kable(digits = 2) %>%
    kableExtra::kable_styling()
}

define aesthetics

palette_condition = c("#ee9b00", "#bb3e03", "#005f73")
palette_topic = c("climate" = "#E6805E",
                 "health" = "#3A3357")
palette_sharing = c("narrowcast sharing" = "#39A2AE",
                    "broadcast sharing" = "#335284")

plot_aes = theme_minimal() +
  theme(legend.position = "top",
        legend.text = element_text(size = 12),
        text = element_text(size = 16, family = "Futura Medium"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.text = element_text(color = "black"),
        axis.line = element_line(colour = "black"),
        axis.ticks.y = element_blank())

load data

data = read.csv("../data/study2_data.csv", stringsAsFactors = FALSE) %>%
  mutate(article_cond = ifelse(article_cond == "social", "other", article_cond),
         msg_rel_self_z = scale(msg_rel_self, center = TRUE, scale = TRUE),
         msg_rel_social_z = scale(msg_rel_social, center = TRUE, scale = TRUE)) %>%
  filter(group == "comment") %>%
  select(-group)

broadasting analyses, comment group only

These analyses explore whether the analyses reported in behavioral study 2 of the main manuscript are moderated by sharing type (narrowcast or broadcast). We focus on the comment group only because this is the only group for which the intervention was effective.

H3

Are the relationships between self and social relevance and sharing intentions moderated by sharing type?

The relationship between self-relevance and sharing intentions was not moderated by topic.

However, the relationship between social relevance and sharing intentions was slightly stronger for health articles compared to climate articles.

data_broadcasting = data %>%
  mutate(sharing_type = ifelse(sharing_type_key == "msg_sharing_broad", 0, 1))

mod_h3 = lmer(msg_share ~ msg_rel_self_z * sharing_type + msg_rel_social_z * sharing_type +
                (1 + msg_rel_self_z + msg_rel_social_z | SID),
              data = data_broadcasting,
              control = lmerControl(optimizer = "bobyqa"))

plot

predicted_h3 = ggeffects::ggpredict(mod_h3, c("msg_rel_self_z", "sharing_type")) %>%
  data.frame() %>%
  mutate(variable = "self-relevance") %>%
  bind_rows(ggeffects::ggpredict(mod_h3, c("msg_rel_social_z", "sharing_type")) %>%
              data.frame() %>%
              mutate(variable = "social relevance")) %>%
  mutate(group = ifelse(group == "0", "broadcast sharing", "narrowcast sharing"))

predicted_sub_h3 = ggeffects::ggpredict(mod_h3, c("msg_rel_self_z", "sharing_type", "SID"), type = "random") %>%
  data.frame() %>%
  mutate(variable = "self-relevance") %>%
  bind_rows(ggeffects::ggpredict(mod_h3, c("msg_rel_social_z", "sharing_type", "SID"), type = "random") %>%
              data.frame() %>%
              mutate(variable = "social relevance")) %>%
  mutate(group = ifelse(group == "0", "broadcast sharing", "narrowcast sharing"))

predicted_h3 %>%
  ggplot(aes(x, predicted, color = group)) +
  stat_smooth(data = predicted_sub_h3, aes(group = interaction(group, facet)),
              geom ='line', method = "lm", alpha = .1, size = .75, se = FALSE) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high, fill = group), alpha = .5, color = NA) +
  geom_line(size = 1.5) +
  facet_grid(~variable) +
  scale_color_manual(name = "", values = palette_sharing) +
  scale_fill_manual(name = "", values = palette_sharing) +
  scale_linetype_manual(name = "", values = c("solid", "dashed")) +
  labs(x = "\nrelevance rating", y = "predicted sharing intention rating\n") +
  plot_aes
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

model table

table_h3_broad = table_model(mod_h3, reversed = TRUE)

table_h3_broad %>%
    kable()  %>%
    kableExtra::kable_styling()
term b [95% CI] df t p
intercept 24.28 [21.23, 27.33] 135.23 15.75 < .001
self-relevance 5.34 [3.30, 7.37] 147.11 5.18 < .001
sharing type (narrowcast) 10.47 [9.06, 11.89] 2564.91 14.51 < .001
social relevance 7.54 [5.30, 9.78] 170.91 6.64 < .001
self-relevance x sharing type (narrowcast) -3.97 [-6.09, -1.85] 2564.91 -3.67 < .001
sharing type (narrowcast) x social relevance 11.41 [9.24, 13.57] 2564.91 10.34 < .001

summary

summary(mod_h3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: msg_share ~ msg_rel_self_z * sharing_type + msg_rel_social_z *  
##     sharing_type + (1 + msg_rel_self_z + msg_rel_social_z | SID)
##    Data: data_broadcasting
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 25391.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5300 -0.5139 -0.0683  0.4373  3.7656 
## 
## Random effects:
##  Groups   Name             Variance Std.Dev. Corr       
##  SID      (Intercept)      255.23   15.976              
##           msg_rel_self_z    34.23    5.851    0.41      
##           msg_rel_social_z  53.67    7.326    0.72 -0.30
##  Residual                  354.74   18.834              
## Number of obs: 2866, groups:  SID, 124
## 
## Fixed effects:
##                                Estimate Std. Error        df t value
## (Intercept)                     24.2786     1.5414  135.2288  15.751
## msg_rel_self_z                   5.3358     1.0310  147.1096   5.175
## sharing_type                    10.4721     0.7215 2564.9077  14.513
## msg_rel_social_z                 7.5382     1.1347  170.9110   6.643
## msg_rel_self_z:sharing_type     -3.9694     1.0803 2564.9077  -3.674
## sharing_type:msg_rel_social_z   11.4056     1.1030 2564.9077  10.341
##                                           Pr(>|t|)    
## (Intercept)                   < 0.0000000000000002 ***
## msg_rel_self_z                      0.000000734299 ***
## sharing_type                  < 0.0000000000000002 ***
## msg_rel_social_z                    0.000000000394 ***
## msg_rel_self_z:sharing_type               0.000243 ***
## sharing_type:msg_rel_social_z < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) msg_rl_sl_ shrng_ msg_rl_sc_ m___:_
## msg_rl_slf_  0.186                                    
## sharing_typ -0.234  0.014                             
## msg_rl_scl_  0.385 -0.606      0.059                  
## msg_rl_s_:_  0.006 -0.524     -0.026  0.372           
## shrng_t:___  0.028  0.402     -0.121 -0.486     -0.766

H5

Here we include both narrowcast and broadcast sharing, and assess potential interactions.

We replicate our previous work in the comment group: the self and social conditions increase sharing intentions compared to the control, and these effects are stronger for narrowcast compared to broadcasting sharing intentions

mod_h5 = lmer(msg_share ~ 1 + article_cond*sharing_type + (1 + article_cond + sharing_type | SID),
              data = data_broadcasting,
              control = lmerControl(optimizer = "bobyqa"))

plot

predicmod_h5 = ggeffects::ggpredict(mod_h5, c("article_cond", "sharing_type")) %>%
  data.frame() %>%
  mutate(x = factor(x, levels = c("self", "control", "other")),
         group = ifelse(group == "0", "broadcast sharing", "narrowcast sharing"))

predicted_mod_h5 = ggeffects::ggpredict(mod_h5, terms = c("article_cond", "sharing_type", "SID"), type = "random") %>%
  data.frame() %>%
  mutate(x = factor(x, levels = c("self", "control", "other")),
         group = ifelse(group == "0", "broadcast sharing", "narrowcast sharing"))

predicmod_h5 %>%
  ggplot(aes(x = x, y = predicted)) +
  stat_summary(data = predicted_mod_h5, aes(group = facet), fun = "mean", geom = "line",
               size = .08, color = "grey50") +
  stat_summary(aes(group = group), fun = "mean", geom = "line", size = 1) +
  geom_pointrange(aes(color = x, ymin = conf.low, ymax = conf.high), size = .5) +
  facet_grid(~group) +
  scale_color_manual(name = "", values = palette_condition, guide = "none") +
  scale_alpha_manual(name = "", values = c(1, .5)) +
  labs(x = "", y = "predicted rating\n") +
  plot_aes

model table

table_h5_broad = table_model(mod_h5, reversed = TRUE)

table_h5_broad %>%
    kable()  %>%
    kableExtra::kable_styling()
term b [95% CI] df t p
intercept 24.68 [19.51, 29.85] 133.24 9.45 < .001
other 6.59 [3.35, 9.84] 242.61 4.00 < .001
self 5.57 [2.27, 8.87] 237.28 3.32 .001
sharing type (narrowcast) 8.19 [4.33, 12.06] 235.91 4.18 < .001
other x sharing type (narrowcast) 8.02 [4.54, 11.50] 2379.96 4.52 < .001
self x sharing type (narrowcast) 3.88 [0.41, 7.35] 2378.91 2.19 .029

summary

summary(mod_h5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: msg_share ~ 1 + article_cond * sharing_type + (1 + article_cond +  
##     sharing_type | SID)
##    Data: data_broadcasting
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 25969.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5635 -0.4688 -0.0001  0.3821  3.6648 
## 
## Random effects:
##  Groups   Name              Variance Std.Dev. Corr             
##  SID      (Intercept)       745.0    27.30                     
##           article_condother 138.9    11.79    -0.09            
##           article_condself  151.4    12.31    -0.10  0.43      
##           sharing_type      277.6    16.66    -0.40  0.08  0.02
##  Residual                   372.0    19.29                     
## Number of obs: 2866, groups:  SID, 124
## 
## Fixed effects:
##                                Estimate Std. Error       df t value
## (Intercept)                      24.681      2.613  133.235   9.446
## article_condother                 6.593      1.647  242.608   4.002
## article_condself                  5.568      1.676  237.283   3.322
## sharing_type                      8.193      1.961  235.909   4.179
## article_condother:sharing_type    8.019      1.775 2379.957   4.517
## article_condself:sharing_type     3.878      1.771 2378.909   2.190
##                                            Pr(>|t|)    
## (Intercept)                    < 0.0000000000000002 ***
## article_condother                        0.00008358 ***
## article_condself                            0.00103 ** 
## sharing_type                             0.00004127 ***
## article_condother:sharing_type           0.00000657 ***
## article_condself:sharing_type               0.02863 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##              (Intr) artcl_cndt artcl_cnds shrng_ artcl_cndt:_
## artcl_cndth  -0.241                                          
## artcl_cndsl  -0.246  0.476                                   
## sharing_typ  -0.443  0.286      0.256                        
## artcl_cndt:_  0.173 -0.539     -0.269     -0.460             
## artcl_cnds:_  0.173 -0.274     -0.529     -0.460  0.508

combined table

table_h3_broad %>% mutate(DV = "H3a-b: Sharing intention") %>%
  bind_rows(table_h5_broad %>% mutate(DV = "H5: Sharing intention")) %>%
  select(DV, everything()) %>%
  kable() %>%
  kable_styling()
DV term b [95% CI] df t p
H3a-b: Sharing intention intercept 24.28 [21.23, 27.33] 135.23 15.75 < .001
H3a-b: Sharing intention self-relevance 5.34 [3.30, 7.37] 147.11 5.18 < .001
H3a-b: Sharing intention sharing type (narrowcast) 10.47 [9.06, 11.89] 2564.91 14.51 < .001
H3a-b: Sharing intention social relevance 7.54 [5.30, 9.78] 170.91 6.64 < .001
H3a-b: Sharing intention self-relevance x sharing type (narrowcast) -3.97 [-6.09, -1.85] 2564.91 -3.67 < .001
H3a-b: Sharing intention sharing type (narrowcast) x social relevance 11.41 [9.24, 13.57] 2564.91 10.34 < .001
H5: Sharing intention intercept 24.68 [19.51, 29.85] 133.24 9.45 < .001
H5: Sharing intention other 6.59 [3.35, 9.84] 242.61 4.00 < .001
H5: Sharing intention self 5.57 [2.27, 8.87] 237.28 3.32 .001
H5: Sharing intention sharing type (narrowcast) 8.19 [4.33, 12.06] 235.91 4.18 < .001
H5: Sharing intention other x sharing type (narrowcast) 8.02 [4.54, 11.50] 2379.96 4.52 < .001
H5: Sharing intention self x sharing type (narrowcast) 3.88 [0.41, 7.35] 2378.91 2.19 .029

moderation by article topic, comment group only

These analyses explore whether the analyses reported in behavioral study 2 of the main manuscript are moderated by article topic (health or climate). We focus on the comment group only because this is the only group for which the intervention was effective.

H2

Are the effects of the experimental manipulations on relevance moderated by article topic?

self-relevance

There is a main effect of topic such that health articles are rated as more self-relevant than climate articles.

The was also an interaction such that the effect of the self-focused condition on self-relevance was weaker for health articles.

mod_h2am = lmer(msg_rel_self ~ article_cond * topic + (1 | SID),
               data = filter(data, sharing_type == 0), 
              control = lmerControl(optimizer = "bobyqa"))

model table

table_h2am = table_model(mod_h2am)

table_h2am %>%
    kable()  %>%
    kableExtra::kable_styling()
term b [95% CI] df t p
intercept 50.64 [45.56, 55.73] 302.50 19.60 < .001
other 6.77 [1.86, 11.68] 1322.45 2.70 .007
self 13.80 [8.96, 18.64] 1321.63 5.60 < .001
topic (health) -1.48 [-6.45, 3.49] 1325.78 -0.58 .559
other x topic (health) -5.15 [-12.24, 1.94] 1336.54 -1.42 .155
self x topic (health) 1.27 [-5.79, 8.32] 1334.55 0.35 .725

summary

summary(mod_h2am)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: msg_rel_self ~ article_cond * topic + (1 | SID)
##    Data: filter(data, sharing_type == 0)
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 13705.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0865 -0.6255  0.1014  0.6929  2.6586 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  SID      (Intercept) 456.2    21.36   
##  Residual             706.4    26.58   
## Number of obs: 1433, groups:  SID, 124
## 
## Fixed effects:
##                               Estimate Std. Error       df t value
## (Intercept)                     50.643      2.584  302.502  19.596
## article_condother                6.769      2.505 1322.445   2.702
## article_condself                13.801      2.465 1321.628   5.598
## topichealth                     -1.480      2.531 1325.784  -0.585
## article_condother:topichealth   -5.146      3.614 1336.537  -1.424
## article_condself:topichealth     1.266      3.598 1334.547   0.352
##                                           Pr(>|t|)    
## (Intercept)                   < 0.0000000000000002 ***
## article_condother                          0.00697 ** 
## article_condself                      0.0000000264 ***
## topichealth                                0.55886    
## article_condother:topichealth              0.15462    
## article_condself:topichealth               0.72505    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) artcl_cndt artcl_cnds tpchlt artcl_cndt:
## artcl_cndth -0.469                                         
## artcl_cndsl -0.477  0.491                                  
## topichealth -0.470  0.499      0.506                       
## artcl_cndt:  0.338 -0.722     -0.354     -0.721            
## artcl_cnds:  0.340 -0.349     -0.714     -0.721  0.505

social relevance

There is a main effect of topic such that health articles are rated as more socially relevant than climate articles.

These data are not consistent with moderation by topic.

mod_h2bm = lmer(msg_rel_social ~ article_cond * topic + (1 | SID),
               data = filter(data, sharing_type == 0), 
              control = lmerControl(optimizer = "bobyqa"))

model table

table_h2bm = table_model(mod_h2bm)

table_h2bm %>%
    kable()  %>%
    kableExtra::kable_styling()
term b [95% CI] df t p
intercept 50.43 [45.48, 55.38] 249.46 20.07 < .001
other 16.13 [11.82, 20.44] 1317.72 7.34 < .001
self 13.23 [8.99, 17.47] 1317.08 6.11 < .001
topic (health) 6.95 [2.59, 11.31] 1320.15 3.13 .002
other x topic (health) -3.79 [-10.01, 2.44] 1328.38 -1.19 .233
self x topic (health) -7.01 [-13.21, -0.81] 1326.76 -2.22 .027

summary

summary(mod_h2bm)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: msg_rel_social ~ article_cond * topic + (1 | SID)
##    Data: filter(data, sharing_type == 0)
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 13368.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3664 -0.4765  0.1157  0.6198  2.5963 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  SID      (Intercept) 496.4    22.28   
##  Residual             543.1    23.30   
## Number of obs: 1433, groups:  SID, 124
## 
## Fixed effects:
##                               Estimate Std. Error       df t value
## (Intercept)                     50.428      2.513  249.457  20.070
## article_condother               16.131      2.198 1317.720   7.338
## article_condself                13.230      2.164 1317.076   6.115
## topichealth                      6.952      2.222 1320.153   3.129
## article_condother:topichealth   -3.786      3.174 1328.381  -1.193
## article_condself:topichealth    -7.007      3.160 1326.763  -2.217
##                                           Pr(>|t|)    
## (Intercept)                   < 0.0000000000000002 ***
## article_condother                0.000000000000378 ***
## article_condself                 0.000000001273007 ***
## topichealth                                0.00179 ** 
## article_condother:topichealth              0.23312    
## article_condself:topichealth               0.02677 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) artcl_cndt artcl_cnds tpchlt artcl_cndt:
## artcl_cndth -0.424                                         
## artcl_cndsl -0.430  0.491                                  
## topichealth -0.424  0.500      0.506                       
## artcl_cndt:  0.306 -0.723     -0.355     -0.721            
## artcl_cnds:  0.307 -0.350     -0.714     -0.722  0.505

combined plot

predicted_h2m = ggeffects::ggpredict(mod_h2am, c("article_cond", "topic")) %>%
  data.frame() %>%
  mutate(model = "self-relevance") %>%
  bind_rows(ggeffects::ggpredict(mod_h2bm, c("article_cond", "topic")) %>%
              data.frame() %>%
              mutate(model = "social relevance")) %>%
  mutate(x = factor(x, levels = c("self", "control", "other")))

predicted_sub_h2m = ggeffects::ggpredict(mod_h2am, terms = c("article_cond", "topic", "SID"), type = "random") %>%
  data.frame() %>%
  mutate(model = "self-relevance") %>%
  bind_rows(ggeffects::ggpredict(mod_h2bm, c("article_cond", "topic", "SID"), type = "random") %>%
              data.frame() %>%
              mutate(model = "social relevance")) %>%
  mutate(x = factor(x, levels = c("self", "control", "other")))
  
predicted_h2m %>%
  ggplot(aes(x = x, y = predicted, color = group)) +
  stat_summary(data = predicted_sub_h2m, aes(group = interaction(group, facet)),
               fun = "mean", geom = "line", size = .1, alpha = .5) +
  stat_summary(aes(group = group), fun = "mean", geom = "line", size = 1) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high, group = group),
                  size = .75) +
  facet_grid(~model) +
  scale_color_manual(name = "", values = palette_topic) +
  labs(x = "", y = "predicted rating\n") +
  plot_aes

H3

Are the relationships between self and social relevance and sharing intentions moderated by article topic?

narrowcasting only

The relationship between self-relevance and sharing intentions was not moderated by topic.

However, the relationship between social relevance and sharing intentions was slightly stronger for health articles compared to climate articles.

mod_h3m = lmer(msg_share ~ msg_rel_self_z * topic + msg_rel_social_z * topic + (1 + msg_rel_self_z + msg_rel_social_z | SID),
              data = filter(data, sharing_type == 0), 
              control = lmerControl(optimizer = "bobyqa"))

plot

predicted_h3m = ggeffects::ggpredict(mod_h3m, c("msg_rel_self_z", "topic")) %>%
  data.frame() %>%
  mutate(variable = "self-relevance") %>%
  bind_rows(ggeffects::ggpredict(mod_h3m, c("msg_rel_social_z", "topic")) %>%
              data.frame() %>%
              mutate(variable = "social relevance"))

predicted_sub_h3 = ggeffects::ggpredict(mod_h3m, terms = c("msg_rel_self_z", "topic", "SID"), type = "random") %>%
  data.frame() %>%
  mutate(variable = "self-relevance") %>%
  bind_rows(ggeffects::ggpredict(mod_h3m, c("msg_rel_social_z", "topic", "SID"), type = "random") %>%
              data.frame() %>%
              mutate(variable = "social relevance"))

predicted_h3m %>%
  ggplot(aes(x, predicted)) +
  stat_smooth(data = predicted_sub_h3, aes(group = interaction(group, facet), color = group),
              geom ='line', method = "lm", alpha = .05, size = .75, se = FALSE) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high, fill = group), alpha = .5, color = NA) +
  geom_line(aes(color = group), size = 1.5) +
  facet_grid(~variable) +
  scale_color_manual(name = "", values = palette_topic) +
  scale_fill_manual(name = "", values = palette_topic) +
  labs(x = "\nrelevance rating", y = "predicted sharing intention rating\n") +
  plot_aes

model table

table_h3m = table_model(mod_h3m)

table_h3m %>%
    kable()  %>%
    kableExtra::kable_styling()
term b [95% CI] df t p
intercept 33.48 [30.40, 36.57] 154.69 21.44 < .001
self-relevance 1.72 [-1.66, 5.10] 196.89 1.00 .317
topic (health) 2.88 [0.87, 4.89] 1287.43 2.81 .005
social relevance 18.00 [14.51, 21.49] 237.09 10.16 < .001
self-relevance x topic (health) 0.79 [-2.79, 4.37] 982.72 0.43 .666
topic (health) x social relevance 1.87 [-1.69, 5.42] 1028.89 1.03 .302

summary

summary(mod_h3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: msg_share ~ msg_rel_self_z * sharing_type + msg_rel_social_z *  
##     sharing_type + (1 + msg_rel_self_z + msg_rel_social_z | SID)
##    Data: data_broadcasting
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 25391.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5300 -0.5139 -0.0683  0.4373  3.7656 
## 
## Random effects:
##  Groups   Name             Variance Std.Dev. Corr       
##  SID      (Intercept)      255.23   15.976              
##           msg_rel_self_z    34.23    5.851    0.41      
##           msg_rel_social_z  53.67    7.326    0.72 -0.30
##  Residual                  354.74   18.834              
## Number of obs: 2866, groups:  SID, 124
## 
## Fixed effects:
##                                Estimate Std. Error        df t value
## (Intercept)                     24.2786     1.5414  135.2288  15.751
## msg_rel_self_z                   5.3358     1.0310  147.1096   5.175
## sharing_type                    10.4721     0.7215 2564.9077  14.513
## msg_rel_social_z                 7.5382     1.1347  170.9110   6.643
## msg_rel_self_z:sharing_type     -3.9694     1.0803 2564.9077  -3.674
## sharing_type:msg_rel_social_z   11.4056     1.1030 2564.9077  10.341
##                                           Pr(>|t|)    
## (Intercept)                   < 0.0000000000000002 ***
## msg_rel_self_z                      0.000000734299 ***
## sharing_type                  < 0.0000000000000002 ***
## msg_rel_social_z                    0.000000000394 ***
## msg_rel_self_z:sharing_type               0.000243 ***
## sharing_type:msg_rel_social_z < 0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) msg_rl_sl_ shrng_ msg_rl_sc_ m___:_
## msg_rl_slf_  0.186                                    
## sharing_typ -0.234  0.014                             
## msg_rl_scl_  0.385 -0.606      0.059                  
## msg_rl_s_:_  0.006 -0.524     -0.026  0.372           
## shrng_t:___  0.028  0.402     -0.121 -0.486     -0.766

narrowcasting and broadcasting

Are the relationships between self and social relevance and sharing intentions moderated by article topic? Do they differ by sharing type?

mod_h3mb = lmer(msg_share ~ msg_rel_self_z*topic*sharing_type + msg_rel_social_z*topic*sharing_type +
                 (1 + msg_rel_self_z + msg_rel_social_z | SID),
              data = data,
              control = lmerControl(optimizer = "bobyqa"))

plot

predicted_h3mb = ggeffects::ggpredict(mod_h3mb, c("msg_rel_self_z", "sharing_type", "topic")) %>%
  data.frame() %>%
  mutate(variable = "self-relevance") %>%
  bind_rows(ggeffects::ggpredict(mod_h3mb, c("msg_rel_social_z", "sharing_type", "topic")) %>%
              data.frame() %>%
              mutate(variable = "social relevance")) %>%
  mutate(group = ifelse(group == "1", "broadcast sharing", "narrowcast sharing"))

predicted_sub_h3mb = ggeffects::ggpredict(mod_h3mb, terms = c("msg_rel_self_z", "sharing_type", "topic", "SID"), type = "random") %>%
  data.frame() %>%
  mutate(variable = "self-relevance") %>%
  bind_rows(ggeffects::ggpredict(mod_h3mb, c("msg_rel_social_z", "sharing_type", "topic", "SID"), type = "random") %>%
              data.frame() %>%
              mutate(variable = "social relevance")) %>%
  mutate(group = ifelse(group == "1", "broadcast sharing", "narrowcast sharing"))

predicted_h3mb %>%
  ggplot(aes(x, predicted)) +
  stat_smooth(data = predicted_sub_h3mb, aes(group = interaction(facet, panel), color = facet),
              geom ='line', method = "lm", alpha = .08, size = .75, se = FALSE) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high, fill = facet), alpha = .3, color = NA) +
  geom_line(aes(color = facet), size = 1.5) +
  facet_grid(group~variable) +
  scale_color_manual(name = "", values = palette_topic) +
  scale_fill_manual(name = "", values = palette_topic) +
  scale_linetype_manual(name = "", values = c("solid", "dashed")) +
  labs(x = "\nrelevance rating", y = "predicted sharing intention rating\n") +
  plot_aes

model table

table_model(mod_h3mb) %>%
    kable()  %>%
    kableExtra::kable_styling()
term b [95% CI] df t p
intercept 33.18 [29.97, 36.40] 167.38 20.38 < .001
self-relevance 0.77 [-2.28, 3.82] 477.44 0.50 .619
topic (health) 3.11 [1.07, 5.16] 2611.55 2.99 .003
sharing type (broadcast) -8.07 [-10.07, -6.08] 2557.39 -7.94 < .001
social relevance 17.93 [14.80, 21.06] 504.66 11.25 < .001
self-relevance x topic (health) 1.79 [-1.64, 5.21] 2140.96 1.02 .306
self-relevance x sharing type (broadcast) 6.51 [2.91, 10.11] 2557.39 3.55 < .001
topic (health) x sharing type (broadcast) -4.97 [-7.81, -2.13] 2557.39 -3.43 < .001
topic (health) x social relevance 1.47 [-1.96, 4.91] 2265.80 0.84 .400
sharing type (broadcast) x social relevance -11.10 [-14.67, -7.54] 2557.39 -6.11 < .001
self-relevance x topic (health) x sharing type (broadcast) -5.25 [-9.71, -0.79] 2557.39 -2.31 .021
topic (health) x sharing type (broadcast) x social relevance -0.51 [-5.03, 4.01] 2557.39 -0.22 .825

summary

summary(mod_h3mb)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## msg_share ~ msg_rel_self_z * topic * sharing_type + msg_rel_social_z *  
##     topic * sharing_type + (1 + msg_rel_self_z + msg_rel_social_z |      SID)
##    Data: data
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 25341.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5146 -0.5146 -0.0599  0.4352  3.7830 
## 
## Random effects:
##  Groups   Name             Variance Std.Dev. Corr       
##  SID      (Intercept)      255.65   15.989              
##           msg_rel_self_z    35.25    5.937    0.41      
##           msg_rel_social_z  54.08    7.354    0.71 -0.31
##  Residual                  350.31   18.717              
## Number of obs: 2866, groups:  SID, 124
## 
## Fixed effects:
##                                            Estimate Std. Error        df
## (Intercept)                                 33.1823     1.6281  167.3838
## msg_rel_self_z                               0.7704     1.5505  477.4418
## topichealth                                  3.1147     1.0420 2611.5454
## sharing_type                                -8.0743     1.0174 2557.3937
## msg_rel_social_z                            17.9330     1.5936  504.6550
## msg_rel_self_z:topichealth                   1.7860     1.7450 2140.9572
## msg_rel_self_z:sharing_type                  6.5125     1.8357 2557.3937
## topichealth:sharing_type                    -4.9662     1.4483 2557.3937
## topichealth:msg_rel_social_z                 1.4724     1.7505 2265.7971
## sharing_type:msg_rel_social_z              -11.1042     1.8173 2557.3937
## msg_rel_self_z:topichealth:sharing_type     -5.2508     2.2762 2557.3937
## topichealth:sharing_type:msg_rel_social_z   -0.5087     2.3057 2557.3937
##                                           t value             Pr(>|t|)    
## (Intercept)                                20.381 < 0.0000000000000002 ***
## msg_rel_self_z                              0.497             0.619498    
## topichealth                                 2.989             0.002824 ** 
## sharing_type                               -7.936  0.00000000000000309 ***
## msg_rel_social_z                           11.253 < 0.0000000000000002 ***
## msg_rel_self_z:topichealth                  1.024             0.306184    
## msg_rel_self_z:sharing_type                 3.548             0.000396 ***
## topichealth:sharing_type                   -3.429             0.000615 ***
## topichealth:msg_rel_social_z                0.841             0.400384    
## sharing_type:msg_rel_social_z              -6.110  0.00000000114728669 ***
## msg_rel_self_z:topichealth:sharing_type    -2.307             0.021143 *  
## topichealth:sharing_type:msg_rel_social_z  -0.221             0.825412    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) msg_rl_sl_ tpchlt shrng_ msg_rl_sc_ ms___: m___:_ tpch:_
## msg_rl_slf_  0.049                                                         
## topichealth -0.317  0.115                                                  
## sharing_typ -0.312  0.099      0.488                                       
## msg_rl_scl_  0.308 -0.758     -0.051 -0.030                                
## msg_rl_sl_:  0.067 -0.742     -0.069 -0.088  0.610                         
## msg_rl_s_:_  0.052 -0.592     -0.082 -0.168  0.480      0.526              
## tpchlth:sh_  0.220 -0.070     -0.695 -0.703  0.021      0.042  0.118       
## tpchlth:___ -0.028  0.622     -0.065  0.027 -0.693     -0.810 -0.437  0.051
## shrng_t:___ -0.017  0.498      0.026  0.053 -0.570     -0.442 -0.841 -0.037
## msg_rl__::_ -0.042  0.477      0.045  0.135 -0.387     -0.652 -0.806 -0.065
## tpchl:_:___  0.013 -0.392      0.054 -0.042  0.449      0.515  0.663 -0.078
##             tp:___ s_:___ m___::
## msg_rl_slf_                     
## topichealth                     
## sharing_typ                     
## msg_rl_scl_                     
## msg_rl_sl_:                     
## msg_rl_s_:_                     
## tpchlth:sh_                     
## tpchlth:___                     
## shrng_t:___  0.519              
## msg_rl__::_  0.520  0.678       
## tpchl:_:___ -0.659 -0.788 -0.790

H5

Are the effects of the experimental manipulations on sharing intentions moderated by article topic?

narrowcasting only

There is a main effect of topic, such that health articles have higher sharing intentions than climate articles.

These data are not consistent with moderation by topic.

mod_h5m = lmer(msg_share ~ article_cond * topic + (1 + topic + article_cond | SID),
              data = data,
              control = lmerControl(optimizer = "bobyqa"))

plot

predicted_h5m = ggeffects::ggpredict(mod_h5m, c("article_cond", "topic")) %>%
  data.frame() %>%
  mutate(x = factor(x, levels = c("self", "control", "other")))

predicted_sub_h5m = ggeffects::ggpredict(mod_h5m, terms = c("article_cond", "topic", "SID"), type = "random") %>%
  data.frame() %>%
  mutate(x = factor(x, levels = c("self", "control", "other")))
  
predicted_h5m %>%
  ggplot(aes(x = x, y = predicted, color = group)) +
  stat_summary(data = predicted_sub_h5m, aes(group = interaction(group, facet)), fun = "mean", geom = "line", size = .1, alpha = .5) +
  stat_summary(aes(group = group), fun = "mean", geom = "line", size = 1) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high, group = group),
                  size = .75) +
  scale_color_manual(name = "", values = palette_topic) +
  labs(x = "", y = "predicted sharing intention\n") +
  plot_aes

model table

table_h5m = table_model(mod_h5m)

table_h5m %>%
    kable()  %>%
    kableExtra::kable_styling()
term b [95% CI] df t p
intercept 28.41 [23.47, 33.36] 137.35 11.37 < .001
other 9.24 [5.80, 12.67] 297.92 5.30 < .001
self 7.79 [4.31, 11.27] 285.63 4.40 < .001
topic (health) 0.79 [-2.66, 4.24] 476.79 0.45 .652
other x topic (health) 2.76 [-1.48, 7.01] 2638.42 1.28 .202
self x topic (health) -0.88 [-5.11, 3.34] 2635.59 -0.41 .682

summary

summary(mod_h5m)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: msg_share ~ article_cond * topic + (1 + topic + article_cond |  
##     SID)
##    Data: data
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 26441.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.8604 -0.5070 -0.0793  0.3668  3.5289 
## 
## Random effects:
##  Groups   Name              Variance Std.Dev. Corr             
##  SID      (Intercept)       643.14   25.360                    
##           topichealth        94.76    9.734   -0.23            
##           article_condother 100.05   10.002    0.04 -0.09      
##           article_condself  118.84   10.901    0.04 -0.36  0.44
##  Residual                   474.36   21.780                    
## Number of obs: 2866, groups:  SID, 124
## 
## Fixed effects:
##                                Estimate Std. Error        df t value
## (Intercept)                     28.4130     2.4999  137.3546  11.366
## article_condother                9.2357     1.7437  297.9161   5.297
## article_condself                 7.7909     1.7698  285.6342   4.402
## topichealth                      0.7935     1.7561  476.7852   0.452
## article_condother:topichealth    2.7627     2.1649 2638.4217   1.276
## article_condself:topichealth    -0.8832     2.1541 2635.5949  -0.410
##                                           Pr(>|t|)    
## (Intercept)                   < 0.0000000000000002 ***
## article_condother                       0.00000023 ***
## article_condself                        0.00001516 ***
## topichealth                                  0.652    
## article_condother:topichealth                0.202    
## article_condself:topichealth                 0.682    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) artcl_cndt artcl_cnds tpchlt artcl_cndt:
## artcl_cndth -0.230                                         
## artcl_cndsl -0.226  0.473                                  
## topichealth -0.355  0.340      0.257                       
## artcl_cndt:  0.206 -0.616     -0.290     -0.621            
## artcl_cnds:  0.208 -0.295     -0.594     -0.621  0.504

narrowcasting and broadcasting

Here we also include broadcast sharing and examine whether the effects of the intervention differ by sharing type and topic

mod_h5mb = lmer(msg_share ~ 1 + article_cond*sharing_type*topic + (1 + article_cond + sharing_type + topic | SID),
              data = data,
              control = lmerControl(optimizer = "bobyqa"))

plot

predicted_h5mb = ggeffects::ggpredict(mod_h5mb, c("article_cond", "topic", "sharing_type")) %>%
  data.frame() %>%
  mutate(x = factor(x, levels = c("self", "control", "other")),
         facet = ifelse(facet == "1", "broadcast sharing", "narrowcast sharing"))

predicted_sub_h5mb = ggeffects::ggpredict(mod_h5mb, terms = c("article_cond", "topic", "sharing_type", "SID"), type = "random") %>%
  data.frame() %>%
  mutate(x = factor(x, levels = c("self", "control", "other")),
         facet = ifelse(facet == "1", "broadcast sharing", "narrowcast sharing"))
  
predicted_h5mb %>%
  ggplot(aes(x = x, y = predicted, color = group)) +
  stat_summary(data = predicted_sub_h5mb, aes(group = interaction(group, panel)), fun = "mean", geom = "line", size = .1, alpha = .5) +
  stat_summary(aes(group = group), fun = "mean", geom = "line", size = 1) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high, group = group), size = .75) +
  facet_grid(~facet) +
  scale_color_manual(name = "", values = palette_topic) +
  labs(x = "", y = "predicted sharing intention\n") +
  plot_aes

model table

table_model(mod_h5mb) %>%
    kable()  %>%
    kableExtra::kable_styling()
term b [95% CI] df t p
intercept 30.79 [25.57, 36.01] 158.35 11.65 < .001
other 13.41 [9.38, 17.45] 551.54 6.53 < .001
self 9.22 [5.17, 13.28] 514.34 4.47 < .001
sharing type (broadcast) -4.75 [-9.21, -0.30] 407.75 -2.10 .037
topic (health) 4.41 [0.40, 8.43] 808.86 2.16 .031
other x sharing type (broadcast) -8.31 [-13.11, -3.51] 2297.58 -3.40 < .001
self x sharing type (broadcast) -3.04 [-7.76, 1.68] 2295.90 -1.26 .207
other x topic (health) 2.19 [-2.81, 7.20] 2421.56 0.86 .390
self x topic (health) -0.03 [-5.02, 4.95] 2423.40 -0.01 .989
sharing type (broadcast) x topic (health) -7.18 [-12.03, -2.33] 2305.31 -2.90 .004
other x sharing type (broadcast) x topic (health) 1.14 [-5.77, 8.06] 2326.91 0.32 .746
self x sharing type (broadcast) x topic (health) -1.43 [-8.31, 5.46] 2323.47 -0.41 .684

summary

summary(mod_h5mb)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## msg_share ~ 1 + article_cond * sharing_type * topic + (1 + article_cond +  
##     sharing_type + topic | SID)
##    Data: data
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 25823.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8410 -0.4638 -0.0216  0.3591  3.6282 
## 
## Random effects:
##  Groups   Name              Variance Std.Dev. Corr                   
##  SID      (Intercept)       681.4    26.10                           
##           article_condother 135.7    11.65    -0.01                  
##           article_condself  153.3    12.38    -0.01  0.46            
##           sharing_type      281.6    16.78    -0.19 -0.06 -0.03      
##           topichealth       117.8    10.85    -0.19 -0.08 -0.29 -0.16
##  Residual                   338.2    18.39                           
## Number of obs: 2866, groups:  SID, 124
## 
## Fixed effects:
##                                              Estimate Std. Error         df
## (Intercept)                                  30.78730    2.64311  158.34815
## article_condother                            13.41194    2.05336  551.53886
## article_condself                              9.22345    2.06419  514.33556
## sharing_type                                 -4.75410    2.26614  407.75410
## topichealth                                   4.41274    2.04452  808.85912
## article_condother:sharing_type               -8.30929    2.44672 2297.57720
## article_condself:sharing_type                -3.03926    2.40847 2295.90315
## article_condother:topichealth                 2.19474    2.55378 2421.55628
## article_condself:topichealth                 -0.03351    2.54240 2423.40222
## sharing_type:topichealth                     -7.17927    2.47182 2305.31347
## article_condother:sharing_type:topichealth    1.14390    3.52477 2326.91370
## article_condself:sharing_type:topichealth    -1.42884    3.51061 2323.46790
##                                            t value             Pr(>|t|)    
## (Intercept)                                 11.648 < 0.0000000000000002 ***
## article_condother                            6.532       0.000000000148 ***
## article_condself                             4.468       0.000009702419 ***
## sharing_type                                -2.098             0.036531 *  
## topichealth                                  2.158             0.031196 *  
## article_condother:sharing_type              -3.396             0.000695 ***
## article_condself:sharing_type               -1.262             0.207111    
## article_condother:topichealth                0.859             0.390201    
## article_condself:topichealth                -0.013             0.989483    
## sharing_type:topichealth                    -2.904             0.003714 ** 
## article_condother:sharing_type:topichealth   0.325             0.745564    
## article_condself:sharing_type:topichealth   -0.407             0.684040    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##               (Intr) artcl_cndt artcl_cnds shrng_ tpchlt artcl_cndt:_
## artcl_cndth   -0.281                                                 
## artcl_cndsl   -0.283  0.481                                          
## sharing_typ   -0.353  0.289      0.299                               
## topichealth   -0.365  0.353      0.295      0.263                    
## artcl_cndt:_   0.223 -0.594     -0.285     -0.523 -0.300             
## artcl_cnds:_   0.227 -0.291     -0.582     -0.531 -0.304  0.491      
## artcl_cndt:    0.231 -0.619     -0.295     -0.259 -0.630  0.497      
## artcl_cnds:    0.232 -0.297     -0.599     -0.261 -0.630  0.240      
## shrng_typ:t    0.223 -0.295     -0.293     -0.523 -0.603  0.498      
## artcl_cndt:_: -0.160  0.428      0.205      0.376  0.434 -0.721      
## artcl_cnds:_: -0.161  0.207      0.414      0.378  0.434 -0.349      
##               artcl_cnds:_ artcl_cndt: artcl_cnds: shrn_: artcl_cndt:_:
## artcl_cndth                                                            
## artcl_cndsl                                                            
## sharing_typ                                                            
## topichealth                                                            
## artcl_cndt:_                                                           
## artcl_cnds:_                                                           
## artcl_cndt:    0.243                                                   
## artcl_cnds:    0.492        0.505                                      
## shrng_typ:t    0.505        0.496       0.497                          
## artcl_cndt:_: -0.354       -0.689      -0.348      -0.720              
## artcl_cnds:_: -0.713       -0.348      -0.690      -0.721  0.506

combined table

table_h2am %>% mutate(DV = "H2a: Self-relevance") %>%
  bind_rows(table_h2bm %>% mutate(DV = "H2b: Social relevance")) %>%
  bind_rows(table_h3m %>% mutate(DV = "H3a-b: Sharing intention")) %>%
  bind_rows(table_h5m %>% mutate(DV = "H5: Sharing intention")) %>%
  select(DV, everything()) %>%
  kable() %>%
  kable_styling()
DV term b [95% CI] df t p
H2a: Self-relevance intercept 50.64 [45.56, 55.73] 302.50 19.60 < .001
H2a: Self-relevance other 6.77 [1.86, 11.68] 1322.45 2.70 .007
H2a: Self-relevance self 13.80 [8.96, 18.64] 1321.63 5.60 < .001
H2a: Self-relevance topic (health) -1.48 [-6.45, 3.49] 1325.78 -0.58 .559
H2a: Self-relevance other x topic (health) -5.15 [-12.24, 1.94] 1336.54 -1.42 .155
H2a: Self-relevance self x topic (health) 1.27 [-5.79, 8.32] 1334.55 0.35 .725
H2b: Social relevance intercept 50.43 [45.48, 55.38] 249.46 20.07 < .001
H2b: Social relevance other 16.13 [11.82, 20.44] 1317.72 7.34 < .001
H2b: Social relevance self 13.23 [8.99, 17.47] 1317.08 6.11 < .001
H2b: Social relevance topic (health) 6.95 [2.59, 11.31] 1320.15 3.13 .002
H2b: Social relevance other x topic (health) -3.79 [-10.01, 2.44] 1328.38 -1.19 .233
H2b: Social relevance self x topic (health) -7.01 [-13.21, -0.81] 1326.76 -2.22 .027
H3a-b: Sharing intention intercept 33.48 [30.40, 36.57] 154.69 21.44 < .001
H3a-b: Sharing intention self-relevance 1.72 [-1.66, 5.10] 196.89 1.00 .317
H3a-b: Sharing intention topic (health) 2.88 [0.87, 4.89] 1287.43 2.81 .005
H3a-b: Sharing intention social relevance 18.00 [14.51, 21.49] 237.09 10.16 < .001
H3a-b: Sharing intention self-relevance x topic (health) 0.79 [-2.79, 4.37] 982.72 0.43 .666
H3a-b: Sharing intention topic (health) x social relevance 1.87 [-1.69, 5.42] 1028.89 1.03 .302
H5: Sharing intention intercept 28.41 [23.47, 33.36] 137.35 11.37 < .001
H5: Sharing intention other 9.24 [5.80, 12.67] 297.92 5.30 < .001
H5: Sharing intention self 7.79 [4.31, 11.27] 285.63 4.40 < .001
H5: Sharing intention topic (health) 0.79 [-2.66, 4.24] 476.79 0.45 .652
H5: Sharing intention other x topic (health) 2.76 [-1.48, 7.01] 2638.42 1.28 .202
H5: Sharing intention self x topic (health) -0.88 [-5.11, 3.34] 2635.59 -0.41 .682

cite packages

report::cite_packages()
##   - Angelo Canty and Brian Ripley (2021). boot: Bootstrap R (S-Plus) Functions. R package version 1.3-28.
##   - Douglas Bates, Martin Maechler and Mikael Jagan (2023). Matrix: Sparse and Dense Matrix Classes and Methods. R package version 1.5-4. https://CRAN.R-project.org/package=Matrix
##   - Douglas Bates, Martin Maechler, Ben Bolker, Steve Walker (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48. doi:10.18637/jss.v067.i01.
##   - Evan Kleiman (2021). EMAtools: Data Management Tools for Real-Time Monitoring/Ecological Momentary Assessment Data. R package version 0.1.4. https://CRAN.R-project.org/package=EMAtools
##   - H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
##   - Hadley Wickham (2021). forcats: Tools for Working with Categorical Variables (Factors). R package version 0.5.1. https://CRAN.R-project.org/package=forcats
##   - Hadley Wickham (2022). stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.5.0. https://CRAN.R-project.org/package=stringr
##   - Hadley Wickham and Maximilian Girlich (2022). tidyr: Tidy Messy Data. R package version 1.2.0. https://CRAN.R-project.org/package=tidyr
##   - Hadley Wickham, Jennifer Bryan and Malcolm Barrett (2021). usethis: Automate Package and Project Setup. R package version 2.1.5. https://CRAN.R-project.org/package=usethis
##   - Hadley Wickham, Jim Hester and Jennifer Bryan (2022). readr: Read Rectangular Text Data. R package version 2.1.2. https://CRAN.R-project.org/package=readr
##   - Hadley Wickham, Jim Hester, Winston Chang and Jennifer Bryan (2021). devtools: Tools to Make Developing R Packages Easier. R package version 2.4.3. https://CRAN.R-project.org/package=devtools
##   - Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2022). dplyr: A Grammar of Data Manipulation. R package version 1.0.9. https://CRAN.R-project.org/package=dplyr
##   - Hao Zhu (2021). kableExtra: Construct Complex Table with 'kable' and Pipe Syntax. R package version 1.3.4. https://CRAN.R-project.org/package=kableExtra
##   - Jim Hester, Hadley Wickham and Gábor Csárdi (2021). fs: Cross-Platform File System Operations Based on 'libuv'. R package version 1.5.2. https://CRAN.R-project.org/package=fs
##   - Kirill Müller and Hadley Wickham (2022). tibble: Simple Data Frames. R package version 3.1.8. https://CRAN.R-project.org/package=tibble
##   - Kuznetsova A, Brockhoff PB, Christensen RHB (2017). "lmerTest Package:Tests in Linear Mixed Effects Models." _Journal of StatisticalSoftware_, *82*(13), 1-26. doi: 10.18637/jss.v082.i13 (URL:https://doi.org/10.18637/jss.v082.i13).
##   - Lionel Henry and Hadley Wickham (2020). purrr: Functional Programming Tools. R package version 0.3.4. https://CRAN.R-project.org/package=purrr
##   - Lüdecke D (2018). "ggeffects: Tidy Data Frames of Marginal Effects fromRegression Models." _Journal of Open Source Software_, *3*(26), 772.doi: 10.21105/joss.00772 (URL: https://doi.org/10.21105/joss.00772).
##   - R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
##   - Rinker, T. W. & Kurkiewicz, D. (2017). pacman: Package Management for R. version 0.5.0. Buffalo, New York. http://github.com/trinker/pacman
##   - Wickham et al., (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686
##   - Yihui Xie (2021). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.37.